The field of medical imaging has seen significant advances since the time X-Rays were first used to determine anatomical abnormalities. Medical imaging hardware has progressed from modern machines, such as Magnetic Resonance (MR) imaging scanners, Computed Tomographic (CT) scanners and Positron Emission Tomographic (PET) scanners, to multimodality imaging systems such as PET-CT and PET-MRI systems. Because of large amount of image data generated by such modern medical scanners, there has been and remains a need for developing image processing techniques that can automate some or all of the processes to determine the presence of anatomical abnormalities in scanned medical images.
Digital medical images are constructed using raw image data obtained from a scanner, for example, a computerized axial tomography (CAT) scanner, magnetic resonance imaging (MRI), etc. Digital medical images are typically either a two-dimensional (“2D”) image made of pixel elements, a three-dimensional (“3D”) image made of volume elements (“voxels”) or a four-dimensional (“4D”) image made of dynamic elements (“doxels”). Such 2D, 3D or 4D images are processed using medical image recognition techniques to determine the presence of anatomical abnormalities or pathologies, such as cysts, tumors, polyps, etc. Given the amount of image data generated by any given image scan, it is preferable that an automatic technique should point out anatomical features in the selected regions of an image to a doctor for further diagnosis of any disease or condition.
Automatic image processing and recognition of structures within a medical image are generally referred to as Computer-Aided Detection (CAD). A CAD system can process medical images, localize and segment anatomical structures, including possible abnormalities (or candidates), for further review. Recognizing anatomical structures within digitized medical images presents multiple challenges. For example, a first concern relates to the accuracy of recognition of anatomical structures within an image. A second area of concern is the speed of recognition. Because medical images are an aid for a doctor to diagnose a disease or condition, the speed with which an image can be processed and structures within that image recognized can be of the utmost importance to the doctor in order to reach an early diagnosis.
Medical images play an important role in the correct diagnosis of diseases, such as Interstitial Lung Disease (ILD). ILD denotes a group of common lung diseases affecting the interstitium. ILD concerns alveolar epithelium, pulmonary capillary endothelium, basement membranes, perivascular and perilymphatic tissues. ILD causes gradual alteration of the lung tissues and leads to breathing dysfunction. It includes, but is not limited to, interstitial pneumonia, emphysema, sarcoidosis, and tuberculosis. FIGS. 1a-c show lung images with different types of ILD. More particularly, FIG. 1a shows an image 102 of a lung with emphysema; FIG. 1b shows an image 104 of a lung with usual interstitial pneumonia; and FIG. 1c shows an image 106 of a lung with sarcoidosis.
With image-based analysis, patients may not need to go through more invasive diagnostic methods such as surgical lung biopsy. One of the most commonly used imaging modalities for diagnosis of ILD is high-resolution computed tomography (HRCT). HRCT has the following advantages compared to other commonly used image modalities, such as X-ray and magnetic resonance imaging (MRI): first, three-dimensional HRCT data avoids superposition of organs and provides high in-plane resolution to facilitate recognition of patterns and distribution of lung tissues; and second, unlike MRI, which is only sensitive to inflammatory changes of pulmonary parenchyma, HRCT accurately depicts image appearance of different disease patterns, such as fibrosis and micro-nodule clusters.
Radiologists typically need to perform multiple screenings for ILD in their daily work. Because of the complex nature of ILD and anatomical variety of different patients, it is hard for radiologists to quickly and accurately make the diagnostic decision for ILD.